Scientists are studying your Twitter slang to help AI

Mathematicians studied 100 billion tweets to help computer algorithms better understand our colloquial digital communication.

Photo credit: Getty Images
  • A group of mathematicians from the University of Vermont used Twitter to examine how young people intentionally stretch out words in text for digital communication.
  • Analyzing the language in roughly 100 billion tweets generated over eight years, the team developed two measurements to assess patterns in the tweets: balance and stretch.
  • The words people stretch are not arbitrary but rather have patterned distributions such as what part of the word is stretched or how much it stretches out.
Keep reading Show less

Why we must teach students to solve big problems


Keep reading Show less

This AI tool measures social distancing. But is more surveillance worth the risk?

The system can even be designed to send alerts to employees when they've come too close to a coworker.

Landing AI/YouTube
  • Since the pandemic began, nations have been using technology in varying degrees to contain the outbreak.
  • This new tool is able to place moving people on a map and estimate the distance between them.
  • Some privacy advocates are raising concerns about private companies and governments installing surveillance technologies.
Keep reading Show less

Ask Sophia the Robot: Is AI an existential threat to humans?

Should humans fear artificial intelligence or welcome it into our lives?

  • Sophia the Robot of Hanson Robotics can mimic human facial expressions and humor, but is that just a cover? Should humans see AI as a threat? She, of course, says no.
  • New technologies are often scary, but ultimately they are just tools. Sophia says that it is the intent of the user that makes them dangerous.
  • The future of artificial intelligence and whether or not it will backfire on humanity is an ongoing debate that one smiling robot won't settle.

Keep reading Show less

An AI can read words in brain signals

Researchers at UCSF have trained an algorithm to parse meaning from neural activity.

Image source: ESB Professional/Shutterstock/Big Think
  • Participants' neural activity is collected as they speak 50 sentences.
  • A machine-learning algorithm develops a prediction of what the collected data means.
  • The system's accuracy varies, but the results are promising.
  • Keep reading Show less